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Linear Operator Theoretic Framework for Data-Driven Optimal Control:
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Classroom Contents
Machine Learning and Dynamical Systems
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- 1 Machine Learning for Prediction of Earth Climate and Weather
- 2 Understanding forecasting with reservoir computing via synchronization
- 3 Kernelization of Reservoir Systems
- 4 Controlling Chaos Using Edge Computing Hardware
- 5 Predicting tipping point with reservoir computing
- 6 Training Autonomous Dynamics of a Soft Body: Embedding Bifurcation Structures...
- 7 State estimation of complex systems
- 8 Learning, approximation and control
- 9 Linear Operator Theoretic Framework for Data-Driven Optimal Control:
- 10 Several topics at the intersection of control, dynamics, and learning from data
- 11 The Operator is the Model
- 12 Koopman-based generalization bound for neural networks
- 13 On the Barriers of Robust Koopman Learning
- 14 Statistical Learning of Transfer Operators and the Infinitesimal Generator
- 15 Representation Learning for Dynamical Systems
- 16 Combinatorial Topological Dynamics
- 17 Identifying nonlinear dynamics with high confidence from sparse data
- 18 On finite-dimensional approximations of push-forwards on locally analytic functionals
- 19 Closed-Loop Koopman Operator Approximation
- 20 Low-dimensional approximations of the conditional law of Volterra processes...
- 21 Data-driven reduced order models of forced systems using invariant foliations
- 22 Should we Derive or Let the Data Drive? Symbiotizing Data-driven Learning...
- 23 Some older, and some current, thoughts on Data and the Modeling of Complex Systems
- 24 Operator Learning Without the Adjoint
- 25 Learning Port Hamiltonian structures using PINNs type architecture
- 26 Provable Posterior Sampling with Score-Based Diffusion through Tilted Transport
- 27 Learning and Dynamical Systems: Perspectives from Optimization, Control, and Robotics
- 28 Learning Coarse-Grained Dynamics on Graph
- 29 Differentiable Programming for Data-driven Modeling, Optimization, and Control
- 30 Detecting non-trivial cycles of point clouds and time series data on manifolds
- 31 Exploring Cancer Progression: From Static Imaging Data to System Dynamics
- 32 Non-smooth dynamics and machine learning
- 33 Avoidance of traps for nonconvex stochastic optimization and equilibrium learning in games
- 34 Non-Euclidean Generative Modeling
- 35 A dynamical systems perspective on measure transport and generative modeling
- 36 Phase Transition Theory fo the Score Degradation of Machine Learning Models
- 37 Dynamical systems in deep generative modelling
- 38 Discovering dynamics and parameters of nonlinear systems from partial observations
- 39 Gated Recurrent Neural Networks with Weighted Time-Delay Feedback
- 40 Continuum Attention for Neural Operators
- 41 Ergodic Basis Pursuit induces robust sparse network reconstruction
- 42 Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections
- 43 Learning Transfer Operators by Kernel Density Estimation
- 44 Graphs of Random Matrices in Deep Learning
- 45 Data-adaptive RKHS regularization for learning kernels in operators
- 46 Ensemble forecasts in reproducing kernel Hilbert space family
- 47 Equivariant learning through invariant theory
- 48 Ensemble forecasts in reproducing kernel Hilbert space family
- 49 Orbit hierarchies and long-term predictability in chaotic systems
- 50 Simplicity bias, algorithmic probability, and time series
- 51 On Bridging Machine Learning, Dynamical Systems, and Algorithmic Information Theory
